Mathew Vis-Dunbar
November 2022
Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.
Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.
This involves abstraction as shapes, colours etc are used represent the data.
Data visualization turns data into visual information. We could turn data into auditory or tactile information as well.
This involves abstraction as shapes, colours etc are used represent the data.
Visual and data literacies are needed to interpret both the data and the abstraction.
Data visualization is the graphical display of abstract information for two purposes: sense-making (also called data analysis) and communication.
Stephen Few. Data Visualization for Human Perception.
Even though an object as a whole might take some conscious effort to identify, the basic visual attributes that combine to make up that object are perceived without any conscious effort.
Stephen Few (2004). Tapping the Power of Visual Perception.
Nominal data
No order
Ordinal data
Intrinsic order
Nominal data
No order
Ordinal data
Intrinsic order
Visualization tool
Frequency plots with bar charts
| Variable | Data type | First 6 values |
|---|---|---|
| Record.number | integer | 1, 2, 3, 4, 5, 6 |
| Survey.year | integer | 2020, 2020, 2020, 2020, 2020, 2020 |
| Survey.month | character | January, January, January, January, January, January |
| Labour.force.status | character | Not in labour force, Employed, at work, Not in labour force, Employed, at work, Employed, at work, Employed, at work |
| Province | character | Ontario, British Columbia, British Columbia, Ontario, Quebec, Ontario |
| Census.metropolitan.area | integer | 0, 0, 9, 0, 0, 3 |
| Age.group | character | 65-69, 60-64, 70 and over, 45-49, 35-39, 30-34 |
| Sex | integer | 2, 2, 1, 2, 2, 2 |
| Highest.educational.attainment | character | Postsecondary certificate or diploma, Above bachelor’s degree, 0 to 8 years, Bachelor’s degree, Postsecondary certificate or diploma, Bachelor’s degree |
| Single.or.multiple.jobholder | character | NA, Single jobholder, NA, Single jobholder, Single jobholder, Single jobholder |
| Class.of.worker..main.job. | character | NA, private sector employees, NA, public sector employees, private sector employees, private sector employees |
| Type.of.work..main.job. | character | NA, Part-time, NA, Full-time, Full-time, Full-time |
| Occupation.at.main.job | character | NA, Sales and service, NA, Management, Manufacturing and utilities, Business, finance and administration |
| Usual.hours.worked.wk.at.main.job | double | NA, 25, NA, 37.5, 40, 33.7 |
| Actual.hours.worked.wk.at.main.job | double | NA, 10, NA, 30, 42, 27.7 |
| Duration.of.unemployment | integer | NA, NA, NA, NA, NA, NA |
| Reason.for.part.time.work | character | NA, Personal preference, NA, NA, NA, NA |
| Reason.for.leaving.job | character | NA, NA, NA, NA, NA, NA |
| Usual.hourly.wages..employees.only. | double | NA, 15, NA, 53.33, 23, 36.52 |
| Job.permanency..employees.only. | character | NA, Permanent, NA, Permanent, Permanent, Permanent |
| Flows.into.unemployment | character | NA, NA, NA, NA, NA, NA |
| Student.status | character | NA, Non-student, NA, Non-student, Non-student, Non-student |
| Statistical.Weight | integer | 279, 235, 201, 217, 93, 696 |
| Variable | Data type | First 6 values |
|---|---|---|
| Record.number | integer | 1, 2, 3, 4, 5, 6 |
| Survey.year | integer | 2020, 2020, 2020, 2020, 2020, 2020 |
| Survey.month | character | January, January, January, January, January, January |
| Labour.force.status | character | Not in labour force, Employed, at work, Not in labour force, Employed, at work, Employed, at work, Employed, at work |
| Province | character | Ontario, British Columbia, British Columbia, Ontario, Quebec, Ontario |
| Census.metropolitan.area | integer | 0, 0, 9, 0, 0, 3 |
| Age.group | character | 65-69, 60-64, 70 and over, 45-49, 35-39, 30-34 |
| Sex | integer | 2, 2, 1, 2, 2, 2 |
| Highest.educational.attainment | character | Postsecondary certificate or diploma, Above bachelor’s degree, 0 to 8 years, Bachelor’s degree, Postsecondary certificate or diploma, Bachelor’s degree |
| Single.or.multiple.jobholder | character | NA, Single jobholder, NA, Single jobholder, Single jobholder, Single jobholder |
| Class.of.worker..main.job. | character | NA, private sector employees, NA, public sector employees, private sector employees, private sector employees |
| Type.of.work..main.job. | character | NA, Part-time, NA, Full-time, Full-time, Full-time |
| Occupation.at.main.job | character | NA, Sales and service, NA, Management, Manufacturing and utilities, Business, finance and administration |
| Usual.hours.worked.wk.at.main.job | double | NA, 25, NA, 37.5, 40, 33.7 |
| Actual.hours.worked.wk.at.main.job | double | NA, 10, NA, 30, 42, 27.7 |
| Duration.of.unemployment | integer | NA, NA, NA, NA, NA, NA |
| Reason.for.part.time.work | character | NA, Personal preference, NA, NA, NA, NA |
| Reason.for.leaving.job | character | NA, NA, NA, NA, NA, NA |
| Usual.hourly.wages..employees.only. | double | NA, 15, NA, 53.33, 23, 36.52 |
| Job.permanency..employees.only. | character | NA, Permanent, NA, Permanent, Permanent, Permanent |
| Flows.into.unemployment | character | NA, NA, NA, NA, NA, NA |
| Student.status | character | NA, Non-student, NA, Non-student, Non-student, Non-student |
| Statistical.Weight | integer | 279, 235, 201, 217, 93, 696 |
| Employment Status | Count |
|---|---|
| Employed, absent from work | 25521 |
| Employed, at work | 174440 |
| Not in labour force | 140433 |
| Unemployed | 20160 |
| Variable | Data type | First 6 values |
|---|---|---|
| Record.number | integer | 1, 2, 3, 4, 5, 6 |
| Survey.year | integer | 2020, 2020, 2020, 2020, 2020, 2020 |
| Survey.month | character | January, January, January, January, January, January |
| Labour.force.status | character | Not in labour force, Employed, at work, Not in labour force, Employed, at work, Employed, at work, Employed, at work |
| Province | character | Ontario, British Columbia, British Columbia, Ontario, Quebec, Ontario |
| Census.metropolitan.area | integer | 0, 0, 9, 0, 0, 3 |
| Age.group | character | 65-69, 60-64, 70 and over, 45-49, 35-39, 30-34 |
| Sex | integer | 2, 2, 1, 2, 2, 2 |
| Highest.educational.attainment | character | Postsecondary certificate or diploma, Above bachelor’s degree, 0 to 8 years, Bachelor’s degree, Postsecondary certificate or diploma, Bachelor’s degree |
| Single.or.multiple.jobholder | character | NA, Single jobholder, NA, Single jobholder, Single jobholder, Single jobholder |
| Class.of.worker..main.job. | character | NA, private sector employees, NA, public sector employees, private sector employees, private sector employees |
| Type.of.work..main.job. | character | NA, Part-time, NA, Full-time, Full-time, Full-time |
| Occupation.at.main.job | character | NA, Sales and service, NA, Management, Manufacturing and utilities, Business, finance and administration |
| Usual.hours.worked.wk.at.main.job | double | NA, 25, NA, 37.5, 40, 33.7 |
| Actual.hours.worked.wk.at.main.job | double | NA, 10, NA, 30, 42, 27.7 |
| Duration.of.unemployment | integer | NA, NA, NA, NA, NA, NA |
| Reason.for.part.time.work | character | NA, Personal preference, NA, NA, NA, NA |
| Reason.for.leaving.job | character | NA, NA, NA, NA, NA, NA |
| Usual.hourly.wages..employees.only. | double | NA, 15, NA, 53.33, 23, 36.52 |
| Job.permanency..employees.only. | character | NA, Permanent, NA, Permanent, Permanent, Permanent |
| Flows.into.unemployment | character | NA, NA, NA, NA, NA, NA |
| Student.status | character | NA, Non-student, NA, Non-student, Non-student, Non-student |
| Statistical.Weight | integer | 279, 235, 201, 217, 93, 696 |
| Education | Count |
|---|---|
| 0 to 8 years | 17299 |
| Above bachelor’s degree | 26337 |
| Bachelor’s degree | 56276 |
| High school graduate | 72725 |
| Postsecondary certificate or diploma | 123358 |
| Some high school | 42341 |
| Some postsecondary | 22218 |
Discrete = Counted
Continuous = Measured
Interval = Greater or less than
Ratio = Percentage more or less
Visualization tool
Frequency plots with histograms
| Variable | Data type | First 6 values |
|---|---|---|
| Record.number | integer | 1, 2, 3, 4, 5, 6 |
| Survey.year | integer | 2020, 2020, 2020, 2020, 2020, 2020 |
| Survey.month | character | January, January, January, January, January, January |
| Labour.force.status | character | Not in labour force, Employed, at work, Not in labour force, Employed, at work, Employed, at work, Employed, at work |
| Province | character | Ontario, British Columbia, British Columbia, Ontario, Quebec, Ontario |
| Census.metropolitan.area | integer | 0, 0, 9, 0, 0, 3 |
| Age.group | character | 65-69, 60-64, 70 and over, 45-49, 35-39, 30-34 |
| Sex | integer | 2, 2, 1, 2, 2, 2 |
| Highest.educational.attainment | character | Postsecondary certificate or diploma, Above bachelor’s degree, 0 to 8 years, Bachelor’s degree, Postsecondary certificate or diploma, Bachelor’s degree |
| Single.or.multiple.jobholder | character | NA, Single jobholder, NA, Single jobholder, Single jobholder, Single jobholder |
| Class.of.worker..main.job. | character | NA, private sector employees, NA, public sector employees, private sector employees, private sector employees |
| Type.of.work..main.job. | character | NA, Part-time, NA, Full-time, Full-time, Full-time |
| Occupation.at.main.job | character | NA, Sales and service, NA, Management, Manufacturing and utilities, Business, finance and administration |
| Usual.hours.worked.wk.at.main.job | double | NA, 25, NA, 37.5, 40, 33.7 |
| Actual.hours.worked.wk.at.main.job | double | NA, 10, NA, 30, 42, 27.7 |
| Duration.of.unemployment | integer | NA, NA, NA, NA, NA, NA |
| Reason.for.part.time.work | character | NA, Personal preference, NA, NA, NA, NA |
| Reason.for.leaving.job | character | NA, NA, NA, NA, NA, NA |
| Usual.hourly.wages..employees.only. | double | NA, 15, NA, 53.33, 23, 36.52 |
| Job.permanency..employees.only. | character | NA, Permanent, NA, Permanent, Permanent, Permanent |
| Flows.into.unemployment | character | NA, NA, NA, NA, NA, NA |
| Student.status | character | NA, Non-student, NA, Non-student, Non-student, Non-student |
| Statistical.Weight | integer | 279, 235, 201, 217, 93, 696 |
15 53 23 37 31 14 13 38 28 27 18 12 13 12 28 37 14 23 12 52 18 49 38 40 26 24 55 20 35 32 15 35 18 27 17 14 16 26 48 12 13 27 8 20 27 23 23 41 30 36 43 32 53 40 41 26 38 58 17 16 36 49 20 50 55 82 28 45 13 55 23 27 9 14 17 51 40 54 46 20 32 31 46 9 12 15 24 16 54 22 13 29 24 35 38 24 27 18 14 35 38 18 38 37 29 41 21 40 16 57 40 59 19 19 16 20 58 23 34 34
15 53 23 37 31 14 13 38 28 27 18 12 13 12 28 37 14 23 12 52 18 49 38 40 26 24 55 20 35 32 15 35 18 27 17 14 16 26 48 12 13 27 8 20 27 23 23 41 30 36 43 32 53 40 41 26 38 58 17 16 36 49 20 50 55 82 28 45 13 55 23 27 9 14 17 51 40 54 46 20 32 31 46 9 12 15 24 16 54 22 13 29 24 35 38 24 27 18 14 35 38 18 38 37 29 41 21 40 16 57 40 59 19 19 16 20 58 23 34 34
15 53 23 37 31 14 13 38 28 27 18 12 13 12 28 37 14 23 12 52 18 49 38 40 26 24 55 20 35 32 15 35 18 27 17 14 16 26 48 12 13 27 8 20 27 23 23 41 30 36 43 32 53 40 41 26 38 58 17 16 36 49 20 50 55 82 28 45 13 55 23 27 9 14 17 51 40 54 46 20 32 31 46 9 12 15 24 16 54 22 13 29 24 35 38 24 27 18 14 35 38 18 38 37 29 41 21 40 16 57 40 59 19 19 16 20 58 23 34 34
| Country | Code | Year | Life.expectency | |
|---|---|---|---|---|
| 7775 | Peru | PER | 2016 | 74.98300 |
| 2366 | Curacao | CUW | 2016 | 77.87317 |
| 1731 | Cape Verde | CPV | 2016 | 72.79800 |
| 5609 | Lesotho | LSO | 2016 | 54.17400 |
| 9941 | Ukraine | UKR | 2016 | 71.47634 |
| 9143 | Sudan | SDN | 2016 | 64.48600 |
| 456 | Australia | AUS | 2016 | 82.50000 |
| 228 | Angola | AGO | 2016 | 61.54700 |
| 7889 | Poland | POL | 2016 | 77.45122 |
| 1960 | China | CHN | 2016 | 76.25200 |
| 6889 | Nepal | NPL | 2016 | 70.25300 |
| 2998 | Estonia | EST | 2016 | 77.73659 |
| 4492 | Iceland | ISL | 2016 | 82.46829 |
| 513 | Austria | AUT | 2016 | 80.89024 |
| 3373 | France | FRA | 2016 | 82.27317 |
| 5723 | Libya | LBY | 2016 | 71.93400 |
| 7547 | Palestine | PSE | 2016 | 73.47300 |
| 5974 | Madagascar | MDG | 2016 | 65.93200 |
| 1389 | Bulgaria | BGR | 2016 | 74.61463 |
| 6946 | Netherlands | NLD | 2016 | 81.50976 |
| Longitude..x. | Latitude..y. | Station.Name | Climate.ID | Date.Time | Year | Month | Day | Data.Quality | Max.Temp..C. |
|---|---|---|---|---|---|---|---|---|---|
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-01 | 2000 | 1 | 1 | NA | -1.0 |
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-02 | 2000 | 1 | 2 | NA | 3.0 |
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-03 | 2000 | 1 | 3 | NA | 0.0 |
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-04 | 2000 | 1 | 4 | NA | 4.5 |
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-05 | 2000 | 1 | 5 | NA | 5.0 |
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-06 | 2000 | 1 | 6 | NA | 0.5 |
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-07 | 2000 | 1 | 7 | NA | 2.5 |
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-08 | 2000 | 1 | 8 | NA | 6.0 |
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-09 | 2000 | 1 | 9 | NA | 4.0 |
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-10 | 2000 | 1 | 10 | NA | 2.5 |
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-11 | 2000 | 1 | 11 | NA | 0.5 |
| -119.4 | 49.86 | KELOWNA EAST | 1123984 | 2000-01-12 | 2000 | 1 | 12 | NA | 3.0 |
| Country | Code | Year | Population | Continent | Life.Expectency | GDP |
|---|---|---|---|---|---|---|
| Lesotho | LSO | 2015 | 2059000 | Africa | 51.038 | 2954 |
| Armenia | ARM | 2015 | 2926000 | Asia | 74.467 | 9552 |
| Uruguay | URY | 2015 | 3412000 | South America | 77.369 | 19668 |
| Slovakia | SVK | 2015 | 5436000 | Europe | 76.827 | 25896 |
| Bosnia and Herzegovina | BIH | 2015 | 3429000 | Europe | 76.865 | 10305 |
| Mali | MLI | 2015 | 17439000 | Africa | 57.509 | 1563 |
| Romania | ROU | 2015 | 19925000 | Europe | 75.476 | 20549 |
| Denmark | DNK | 2015 | 5689000 | Europe | 80.475 | 44939 |
| Jamaica | JAM | 2015 | 2891000 | North America | 74.098 | 7115 |
| Senegal | SEN | 2015 | 14578000 | Africa | 66.747 | 2446 |
| Eswatini | SWZ | 2015 | 1104000 | Africa | 55.359 | 7726 |
| Pakistan | PAK | 2015 | 199427008 | Asia | 66.577 | 5056 |
| Gambia | GMB | 2015 | 2086000 | Africa | 60.910 | 1948 |
| Haiti | HTI | 2015 | 10696000 | North America | 62.485 | 1649 |
| Vietnam | VNM | 2015 | 92677000 | Asia | 75.110 | 5733 |
| Congo | COG | 2015 | 4856000 | Africa | 63.097 | 4526 |
| Gabon | GAB | 2015 | 1948000 | Africa | 64.913 | 14315 |
| Nepal | NPL | 2015 | 27015000 | Asia | 69.515 | 2607 |
| North Macedonia | MKD | 2015 | 2079000 | Europe | 75.406 | 13586 |
| Cyprus | CYP | 2015 | 1161000 | Europe | 80.350 | 25903 |
| Country | Code | Year | Population | Continent | Life.Expectency | GDP |
|---|---|---|---|---|---|---|
| Lesotho | LSO | 2015 | 2059000 | Africa | 51.038 | 2954 |
| Armenia | ARM | 2015 | 2926000 | Asia | 74.467 | 9552 |
| Uruguay | URY | 2015 | 3412000 | South America | 77.369 | 19668 |
| Slovakia | SVK | 2015 | 5436000 | Europe | 76.827 | 25896 |
| Bosnia and Herzegovina | BIH | 2015 | 3429000 | Europe | 76.865 | 10305 |
| Mali | MLI | 2015 | 17439000 | Africa | 57.509 | 1563 |
| Romania | ROU | 2015 | 19925000 | Europe | 75.476 | 20549 |
| Denmark | DNK | 2015 | 5689000 | Europe | 80.475 | 44939 |
| Jamaica | JAM | 2015 | 2891000 | North America | 74.098 | 7115 |
| Senegal | SEN | 2015 | 14578000 | Africa | 66.747 | 2446 |
| Eswatini | SWZ | 2015 | 1104000 | Africa | 55.359 | 7726 |
| Pakistan | PAK | 2015 | 199427008 | Asia | 66.577 | 5056 |
| Gambia | GMB | 2015 | 2086000 | Africa | 60.910 | 1948 |
| Haiti | HTI | 2015 | 10696000 | North America | 62.485 | 1649 |
| Vietnam | VNM | 2015 | 92677000 | Asia | 75.110 | 5733 |
| Congo | COG | 2015 | 4856000 | Africa | 63.097 | 4526 |
| Gabon | GAB | 2015 | 1948000 | Africa | 64.913 | 14315 |
| Nepal | NPL | 2015 | 27015000 | Asia | 69.515 | 2607 |
| North Macedonia | MKD | 2015 | 2079000 | Europe | 75.406 | 13586 |
| Cyprus | CYP | 2015 | 1161000 | Europe | 80.350 | 25903 |
| Country | Code | Year | Population | Continent | Life.Expectency | GDP |
|---|---|---|---|---|---|---|
| Lesotho | LSO | 2015 | 2059000 | Africa | 51.038 | 2954 |
| Armenia | ARM | 2015 | 2926000 | Asia | 74.467 | 9552 |
| Uruguay | URY | 2015 | 3412000 | South America | 77.369 | 19668 |
| Slovakia | SVK | 2015 | 5436000 | Europe | 76.827 | 25896 |
| Bosnia and Herzegovina | BIH | 2015 | 3429000 | Europe | 76.865 | 10305 |
| Mali | MLI | 2015 | 17439000 | Africa | 57.509 | 1563 |
| Romania | ROU | 2015 | 19925000 | Europe | 75.476 | 20549 |
| Denmark | DNK | 2015 | 5689000 | Europe | 80.475 | 44939 |
| Jamaica | JAM | 2015 | 2891000 | North America | 74.098 | 7115 |
| Senegal | SEN | 2015 | 14578000 | Africa | 66.747 | 2446 |
| Eswatini | SWZ | 2015 | 1104000 | Africa | 55.359 | 7726 |
| Pakistan | PAK | 2015 | 199427008 | Asia | 66.577 | 5056 |
| Gambia | GMB | 2015 | 2086000 | Africa | 60.910 | 1948 |
| Haiti | HTI | 2015 | 10696000 | North America | 62.485 | 1649 |
| Vietnam | VNM | 2015 | 92677000 | Asia | 75.110 | 5733 |
| Congo | COG | 2015 | 4856000 | Africa | 63.097 | 4526 |
| Gabon | GAB | 2015 | 1948000 | Africa | 64.913 | 14315 |
| Nepal | NPL | 2015 | 27015000 | Asia | 69.515 | 2607 |
| North Macedonia | MKD | 2015 | 2079000 | Europe | 75.406 | 13586 |
| Cyprus | CYP | 2015 | 1161000 | Europe | 80.350 | 25903 |
| Country | Code | Year | Population | Continent | Life.Expectency | GDP |
|---|---|---|---|---|---|---|
| Lesotho | LSO | 2015 | 2059000 | Africa | 51.038 | 2954 |
| Armenia | ARM | 2015 | 2926000 | Asia | 74.467 | 9552 |
| Uruguay | URY | 2015 | 3412000 | South America | 77.369 | 19668 |
| Slovakia | SVK | 2015 | 5436000 | Europe | 76.827 | 25896 |
| Bosnia and Herzegovina | BIH | 2015 | 3429000 | Europe | 76.865 | 10305 |
| Mali | MLI | 2015 | 17439000 | Africa | 57.509 | 1563 |
| Romania | ROU | 2015 | 19925000 | Europe | 75.476 | 20549 |
| Denmark | DNK | 2015 | 5689000 | Europe | 80.475 | 44939 |
| Jamaica | JAM | 2015 | 2891000 | North America | 74.098 | 7115 |
| Senegal | SEN | 2015 | 14578000 | Africa | 66.747 | 2446 |
| Eswatini | SWZ | 2015 | 1104000 | Africa | 55.359 | 7726 |
| Pakistan | PAK | 2015 | 199427008 | Asia | 66.577 | 5056 |
| Gambia | GMB | 2015 | 2086000 | Africa | 60.910 | 1948 |
| Haiti | HTI | 2015 | 10696000 | North America | 62.485 | 1649 |
| Vietnam | VNM | 2015 | 92677000 | Asia | 75.110 | 5733 |
| Congo | COG | 2015 | 4856000 | Africa | 63.097 | 4526 |
| Gabon | GAB | 2015 | 1948000 | Africa | 64.913 | 14315 |
| Nepal | NPL | 2015 | 27015000 | Asia | 69.515 | 2607 |
| North Macedonia | MKD | 2015 | 2079000 | Europe | 75.406 | 13586 |
| Cyprus | CYP | 2015 | 1161000 | Europe | 80.350 | 25903 |
Colour should be meaningful and take into account the nature of the data being graphed. It should also be attune to colour blindness.
Sequential
Diverging
Qualitative
ColorBrewer https://colorbrewer2.org/
Who would be responsible for
Who would have an interest in keeping a record of the data
Purchased and leased data sets are available in a couple of ways: